Growth Monitoring Program and Children Wasting in Nepal

1. Introduction

Research Question

Is participation in the Nepalese Government’s Growth Monitoring Programs (GMP) associated with reduced wasting (acute malnutrition) in children under five years of age in Nepal?

Motivation for the Study

Wasting, or acute malnutrition, is a critical public health issue in Nepal, affecting the growth and development of children under five. Despite various interventions, the prevalence of wasting remains high. Growth Monitoring Programs (GMP) are designed to track the growth and nutritional status of children, providing timely interventions to prevent malnutrition. However, the effectiveness of these programs in reducing wasting has not been thoroughly evaluated. This study aims to fill this gap by analyzing the association between GMP participation and wasting, thereby providing evidence to inform policy and improve program implementation.

In addition to the challenges of malnutrition, the 2015 earthquake in Nepal had a profound impact on the country’s public health infrastructure, including the implementation of health programs such as the GMP. The earthquake disrupted the delivery of health services and likely affected the participation rates in the GMP, particularly in the most severely affected areas. This event introduces an important contextual factor that must be considered when evaluating the effectiveness of the GMP, as it may have skewed results and influenced the outcomes of this study.

2. Policy Background

The Nepalese Government has been proactive in addressing malnutrition through various policies and programs. The Growth Monitoring Program (GMP) is a key component of these efforts, integrated within the broader framework of the National Multi-Sector Nutrition Plan (MSNP). Launched in 2013, the MSNP aims to improve the nutritional status of women and children by addressing the underlying causes of malnutrition through a coordinated, multi-sectoral approach.

Growth Monitoring Program (GMP)

• Objective:
  • The primary goal of GMP is to monitor the growth and development of children under five years of age, identify growth faltering early, and provide timely interventions.
• Implementation:
  • GMP is implemented through health posts and community health workers who:
    • Regularly measure children’s weight and height.
    • Plot these measurements on growth charts.
    • Provide counseling to caregivers.
  • Children identified as at risk of malnutrition are referred to appropriate health and nutrition services.
• Components:
  • Regular growth monitoring sessions.
  • Nutrition education and counseling for caregivers on appropriate feeding practices, hygiene, and health care.

Alignment with Global Commitments

Nepal’s nutrition policies align with global commitments, including the Sustainable Development Goals (SDGs):

• SDG 2: Zero Hunger
• SDG 3: Good Health and Well-being

Additionally, Nepal is a signatory to the Scaling Up Nutrition (SUN) Movement, which supports national efforts to improve nutrition. Despite progress, challenges remain:

• Limited resources
• Geographic barriers
• Socio-cultural factors that affect the implementation and effectiveness of nutrition programs.

3. Data description

For this study, we utilize data from the annual surveys conducted as part of the PoSHAN Community Studies in Nepal. The PoSHAN Community Studies is a public health research project of the Feed the Future Innovation Lab for Collaborative Research on Nutrition (Nutrition Innovation Lab), funded by the US Agency for International Development (USAID). These surveys provide nationally representative data on the food security and nutritional status of rural families with young children.

The surveys are conducted annually from May to July and include the following key components:

• Food Security Assessments
• Nutritional Status of Young Children
• Household Characteristics
• Health and Nutrition Practices
• Agro-ecological Contexts

Key Components

• Household Interviews
  Collect detailed information on demographics, socioeconomic status, agricultural activities, dietary intake, and health practices.

• Anthropometric Measurements
  Measure the height, weight, and mid-upper arm circumference of children and women to assess nutritional status.

• Biochemical Assessments
  Collect blood samples to analyze micronutrient levels and other health indicators.

3.1 Representative Population

The annual surveys cover a representative sample of households across Nepal’s three major agro-ecological zones: mountains, hills, and terai. The sampling frame includes all districts and Village Development Committees (VDCs) in Nepal, with systematic random sampling used to select 7 VDCs from each zone. Dataset.

The study population includes households with children under five years of age and newly married women. Since the focus of our study is the wasting status of children under 5, we excluded the households with just newly married women for our explorations.

3.2 Challenges With Data

The unit of analysis for this study was child-year, which required each household ID to be unique and consistent across years. However, when we inspected the data, we encountered inconsistencies in the dataset from what was outlined in the study manual, as the household IDs were not consistently matched over time. This issue could be due to randomization of household IDs to protect participant identities or other factors that were not immediately clear to us. Unfortunately, we were unable to reach out to the data source providers for clarification.

Given this limitation, we worked with the publicly available data and developed a unique identifier for each child. We used stable characteristics such as region, district, cluster, ethnicity, religion, gender, child’s age, mother’s age, and birth order, and accounted for changes in dynamic characteristics like the child’s age and mother’s age over time.

3.2 Definitions of Key Variables

Treatment Variable (X1)

• Participation in the Growth Monitoring Program (GMP):
  • Dummy variable indicating whether a child has participated in GMP (Yes/No).
  • Groups: Participants vs. Non-participants.

Outcome Variable (Y)

• Wasting (Acute Malnutrition):
  • Dichotomous variable representing the wasting status of children, measured by weight-for-height Z-scores.
  • Groups: Wasted vs. Not wasted.

Key Covariates

1. Demographic Factors:
   • Age of the child.
   • Sex of the child.
   • Age of the mother.
2. Socioeconomic Status:
   • Household income level.
3. Geographic Location:
   • Agro-ecological zones (mountains, hills, terai).
4. Health and Nutrition Practices:
   • Breastfeeding practices.

Distribution of Key Variables

(Participation in GMP and Wasting Status)

A total of 16,091 children did not participate, while only 3,854 participated. This highlights a significant gap in program participation.

The majority, 16,761 children, are not wasted, while 3,184 are identified as wasted.

3.4 Overall Participation and Wasting Rate by Geographic Location

Across all regions, children who did not participate consistently had higher numbers of wasting cases compared to participants. The Terai region shows the highest number of wasted children overall, while the Mountains and Hills have smaller but noticeable variations over the years.

Regional Participation and Wasting Rates

Region	n	Participated	Did_not_Participate	Wasted	Not_Wasted
Mountains	2750	0.39	0.61	0.07	0.93
Hills	4110	0.35	0.65	0.08	0.92
Terai	13085	0.10	0.90	0.20	0.80

The Table 1 above shows that the participation in the growth monitoring program (GMP) varies significantly across regions, with the Mountains and Hills showing higher participation rates (39% and 35%, respectively) compared to the Terai (10%). Wasting rates are notably higher in the Terai at 20%, while the Mountains and Hills report lower rates at 7% and 8%, respectively. These results highlight disparities in both program participation and child health outcomes across regions.

Participation in GMP and wasting status were compared across the treatment (participants) and control (non-participants) groups. Table 2 below presents the mean values of key covariates, allowing us to assess baseline differences between the two groups.

Descriptive Statistics Comparing Individual and Household Characteristics for Wasting Children by Treatment Status and Year

Treatment Status	Year	Mean Wasting	Mean Income	Mean Child Age	Mean Child Sex	Mean Mother Age	Mean Breastfed Day
0	2013	0.184	9292.012	3.233	0.468	27.264	1.707
1	2013	0.154	8965.138	2.446	0.490	25.718	2.066
0	2014	0.174	13303.440	3.252	0.469	27.133	1.749
1	2014	0.123	16127.369	2.427	0.478	25.682	2.235
0	2015	0.167	19037.126	3.158	0.464	26.554	1.731
1	2015	0.155	17297.664	2.457	0.473	25.093	2.068
0	2016	0.146	27538.173	3.232	0.467	26.964	1.732
1	2016	0.106	29858.939	2.704	0.467	26.216	1.916

3.5 Overall Participation and Wasting Rate by Household Income

Participation and Wasting Rates by Income Level

Income_Level	n	Participated	Did_not_Participate	Wasted	Not_Wasted
Low Income	3223	0.18	0.82	0.19	0.81
Middle Income	9406	0.20	0.80	0.15	0.85
No Income	6940	0.20	0.80	0.17	0.83
Upper Middle Income	376	0.20	0.80	0.10	0.90

4. Empirical Strategy

Regression Models

To estimate the impact of GMP participation on reducing wasting in children, we employed four regression models.

1. Simple Linear Probability Model

\(\underline{\text{Model 1}}\), a simple linear probability model, served as a baseline to understand the direct association between GMP participation and wasting without accounting for fixed effects.

2. Two-Way Fixed Effect Model

\(\underline{\text{Model 2}}\), utilized a Two-Way Fixed Effects Difference-in-Differences (TWFE DID) approach, incorporating child fixed effects and year fixed effects. This model controlled for unobserved individual characteristics and time-specific effects that could influence the outcomes.

3. Event Study Two-Way Fixed Effect

\(\underline{\text{Model 3}}\), addressed the dynamic nature of participation in our longitudinal dataset, where children could “enter” or “exit” the panel at different times (e.g., joining the GMP in 2014 or dropping out in 2015). Recognizing the potential influence of unobservable factors, such as household characteristics, local program availability, or child health conditions, we employed an event study framework with a Two-Way Fixed Effects (TWFE) approach. This framework is particularly well-suited to panel data with staggered treatment adoption, accommodating variations in the timing and duration of program participation.

This methodology allowed us to reframe the time variable relative to each child’s initial participation in the GMP, enabling us to estimate treatment effects both before and after enrollment. Children who never participated served as a comparison group, with a baseline period (-1) assigned to ensure consistency in temporal comparisons. By controlling for individual and time-fixed effects, and incorporating observable characteristics that may influence participation, the TWFE approach mitigated potential bias from differential selection and provided a better framework for evaluating the effects of GMP participation on child wasting outcomes.

To better understand the relationship between time to treatment and key variables, the time to treatment is categorized as follows:

• -2: Children who missed the treatment two times
• -1: Children who never participated
• 0: Children who participated in one year
• 1: Children who participated in two years
• 2: Children who participated in three years
• 3: Children who participated in all four years

The following table summarizes the mean values of key variables across different time windows relative to GMP participation:

Summary Statistics by Time to Treatment Adjustment

Time to Treatment (Adjusted)	Mean Wasting	Mean Total Income	Mean Child Age	Mean Child Sex	Mean Mother Age	Mean Breastfed Day
-2	0.000	20544.67	2.267	0.467	24.400	2.400
-1	0.168	17216.14	3.222	0.467	27.001	1.731
0	0.136	18528.04	2.480	0.478	25.790	2.080
1	0.081	16416.92	3.019	0.453	25.801	1.888
2	0.032	23665.97	3.613	0.452	25.758	1.581
3	0.050	30107.50	4.450	0.500	28.000	1.350

The decline in wasting rates with increased participation suggests a positive association between program engagement and child nutrition. However, the slight uptick in wasting for children participating in all four years may warrant further investigation into the profiles of these children or program design.

Higher participation among wealthier households suggests potential barriers (e.g., economic, logistical) for lower-income families. The observed trends indicate that older mothers and older children are more likely to sustain program participation. The decline in breastfeeding with increased program participation could reflect a natural progression with age or indicate a gap in reinforcing breastfeeding messages in the program.

4. Sensitivity analysis

\(\underline{\text{Model 4}}\), employs a Two-Way Fixed Effects (TWFE) Event Study Approach with time to treatment, child fixed effects (FE), and year fixed effects (without 2015). This model, which served as a sensitivity analysis to examine our results in the light of the unique circumstances of the 2015 earthquake, which disrupted participation in the GMP and may have introduced bias into the analysis, especially with regards to the most affected regions and vulnerable groups, resulting in potential high-level attrition. Specifically, we excluded 2015 from the dataset to assess whether the earthquake had a confounding effect on the estimated treatment impacts. During 2015, sample size in Mountains and Hills decreased dramatically, notably disproportionately affecting the untreated group. Subsequently, a higher “bump” in wasting was observed in the treatment group, which retained its relative presence in the sample as compared to the control group, and resulted in a spike of wasting in 2015, as the selection to the treatment group was based on targeted approach.

The following is a table of key estimates from all four models.

Summary of Models 1–5: Coefficients, Standard Errors, and p-values for the ‘Treated’ Variable with Significance Levels

Model	Beta1	SE	p.value	Significance
Linear Probability Mode	0.000	0.007	0.990	Not Significant
TWFE DID with Child FE and Year FE	1.809	24602.062	1.000	Not Significant
TWFE Event Study	1.995	24339.793	1.000	Not Significant
TWFE DID without 2015	-1.184	2158.342	1.000	Not Significant
TWFE Event Study without 2015	-1.629	2124.356	0.999	Not Significant

While the exclusion of 2015 resulted in a change in the directionality of the estimated treatment effects, the observed impact should be interpreted with caution. The direction of the effects in Model 4 suggests that the earthquake could have had a substantial influence on the treatment dynamics, particularly for the most affected regions.

The change in directionality between Models 3 and 4 highlights the sensitivity of the results to the inclusion of 2015 in the dataset. While the results of Model 3 provide the most comprehensive view of the treatment effects, Model 4 provides an important robustness check by exploring whether the earthquake in 2015 significantly altered the treatment dynamics. Given the substantial differences in participation and sample attrition in 2015, Model 4’s results should be interpreted as showing how sensitive the treatment effect is to the inclusion of that particular year.

Although none of the models yielded statistically significant results, the results from Model 3 are preferred because it uses the full dataset and provides a more complete understanding of the treatment effects over time. Model 4, while informative, shows that the 2015 earthquake may have introduced confounding effects that need further exploration.

Econometric Methods and Control Variables

Now that we’ve chosen Model 3 as the preferred specification, we proceed with detailing the econometric methods and control variables used. To investigate whether participation in GMP is associated with reduced wasting (acute malnutrition) in children under five years of age, we employ a Two-Way Fixed Effects Difference-in-Differences (TWFE DID) approach allowing us to control for both time-invariant individual characteristics and time-specific effects, thereby isolating the causal impact of the GMP on child wasting.

To investigate whether participation in GMP is associated with reduced wasting (acute malnutrition) in children under five years of age, we employ a Two-Way Fixed Effects Difference-in-Differences (TWFE DID) approach. This method allows us to:

• Control for time-invariant individual characteristics.
• Account for time-specific effects, isolating the causal impact of GMP participation on child wasting.

The inclusion of relative time-to-treatment dummies further strengthens the specification by capturing dynamic effects associated with treatment timing.

In addition to the treatment variable and relative time-to-treatment dummies, the following control variables were included to mitigate potential confounders:

1. Child-specific characteristics:
   • Age of the child
   • Gender of the child
2. Maternal characteristics:
   • Age of the mother
3. Breastfeeding frequency:
   • Number of times the child is breastfed per day
4. Ethnicity:
   • Ethnicity of the child
5. Household income:
   • Total household income
6. Geographic indicators:
   • Region where the child’s household is located
   • District where the child’s household is located

These control variables were selected to account for demographic, socioeconomic, and geographic variations that might influence wasting outcomes independently of GMP participation. By including these controls, we aim to minimize bias and strengthen the validity of our findings.

2. Pairwise Correlation Analysis

To ensure the robustness of our model, we conducted a pairwise correlation analysis between the key variables, including the treatment indicator and controls. The results are summarized below.

The low-to-moderate correlations indicate that multicollinearity is not a concern, supporting the validity of our inclusion of these controls.

Before proceeding with the regression analysis, we conducted a Variance Inflation Factor (VIF) test to assess multicollinearity among the independent variables, particularly the control variables such as household income, region, breastfeeding frequency, and maternal age. Multicollinearity can bias the estimated coefficients and inflate standard errors, leading to unreliable statistical inferences.

Variance Inflation Factor (VIF) for Logistic Regression Model

Variable	GVIF	Df	Adjusted GVIF
growthpro	1.162	1	1.078
child_age	2.377	1	1.542
child_sex	1.010	1	1.005
mother_age	1.100	1	1.049
breastfed_day	2.312	3	1.150
ethnicity	4.085	10	1.073
region	3.894	2	1.405
income_lvl	1.009	1	1.004
year	1.056	3	1.009

The results of the VIF test indicated that all variables had VIF values below 5, suggesting that multicollinearity was not a significant concern in our models. This finding was consistent with the low-to-moderate correlations observed in the pairwise correlation table.

Primary Model Specification

Two-Way Fixed Effects Difference-in-Differences (TWFE DID) Approach Model Specification:

\[ \begin{aligned} \mathrm{wasting}_i &= \beta_0 + \beta_1 \cdot \mathrm{treated}_{it} + \sum_{j} \gamma_j \cdot \mathrm{time\_to\_treatment}_{it} \\ &\quad + \beta_2 \cdot \mathrm{child\_age}_{it} + \beta_3 \cdot \mathrm{child\_gender}_{it} + \beta_4 \cdot \mathrm{mother\_age}_{it} \\ &\quad + \beta_5 \cdot \mathrm{breastfed\_day}_{it} + \beta_6 \cdot \mathrm{ethnicity}_{it} + \beta_7 \cdot \mathrm{total\_income}_{it} \\ &\quad + \beta_8 \cdot \mathrm{region}_{it} + \beta_9 \cdot \mathrm{district}_{it} + \mu_i + \tau_t + \varepsilon_{it} \end{aligned} \]

Coefficients

• \(\mathrm{wasting}_{it}\): Dichotomous dependent variable indicating the status of wasting for child (i) at time (t).
• \(\mathrm{treated}_{it}\): Binary independent variable indicating whether child (i) received the treatment at time (t).
• \(\mathrm{time\_to\_treatment}_{it}\): Set of dummy variables representing the relative time to treatment, with (-1) as the reference category.
• \(\mathrm{child\_age}_{it}\): Control variable indicating age of child.
• \(\mathrm{child\_gender}_{it}\): Control variable indicating whether the child is a girl or a boy.
• \(\mathrm{mother\_age}_{it}\): Control variable indicating age of mother.
• \(\mathrm{breastfed\_day}_{it}\): Control variable indicating number of times the child is breastfed per day.
• \(\mathrm{ethnicity}_{it}\): Control variable indicating ethnicity of child.
• \(\mathrm{total\_income}_{it}\): Control variable indicating total household income.
• \(\mathrm{region}_{it}\): Control variable indicating region where child’s household is located.
• \(\mathrm{district}_{it}\): Control variable indicating district where child’s household is located.
• \(\mu_i\): Household fixed effects to control for unobserved household-level characteristics.
• \(\tau_t\): Year fixed effects to control for time-specific effects.
• \(\varepsilon_{it}\): Error term for unobserved factors affecting wasting.

Key Considerations

• Individual Fixed Effects (FEs):
  Estimation includes only individuals who change treatment status to avoid perfect collinearity between individual FEs and the treatment variable.

• Survey Wave Inclusion:
  It is acceptable if individuals appear in the first wave of the survey or not, as this does not affect the initial estimation.

The regression output for our chosen model is as follows:

Model 3 - Event Study TWFE

	Estimate	Std. Error	t value	Pr(>|t|)	Significance
treated	1.995	24339.793	0.000	1.000
time_to_treatment_adj::-2	-0.129	0.100	-1.283	0.199
time_to_treatment_adj::0	0.074	0.058	1.284	0.199
time_to_treatment_adj::1	0.052	0.066	0.789	0.430
time_to_treatment_adj::2	0.060	0.070	0.863	0.388
time_to_treatment_adj::3	0.158	0.085	1.847	0.065
child_age	-0.007	0.006	-1.134	0.257
child_sex_cleanGirl	-0.005	0.011	-0.411	0.681
breastfed_day_clean1-10 times	0.015	0.015	1.010	0.312
breastfed_day_clean11-20 times	0.071	0.020	3.492	0.000	***
breastfed_day_clean21 or more times	0.043	0.029	1.512	0.131
ethnicity_cleanNewar	-0.078	1958.478	0.000	1.000
ethnicity_cleanTerai Brahmin/Chhettri	-0.047	23207.007	0.000	1.000
total_income_clean	0.000	0.000	0.500	0.617

6. Findings

Treatment Effect

The coefficient for the treated variable was positive but statistically insignificant, with a p-value of 1.000. This suggests that, in the sample, the direct effect of participation in the GMP on child wasting is not statistically significant. This finding could imply that the GMP, as currently implemented, does not have a discernible impact on reducing child wasting in this study’s context. However, this result requires further consideration, especially in light of the high standard error associated with this estimate, which suggests substantial variability in the data. Also, given that the sensitivity analysis showed that the 2015 earthquake may have introduced confounding effects that need further exploration, it is important to note that a positive effect of treatment on wasting seems possible.

Timing of Treatment

The time_to_treatment_adj dummies, which capture the effect of treatment at different time points relative to the treatment, also showed non-significant results. While the coefficient for the time-to-treatment variable at time 3 (relative to treatment) was marginally significant (p = 0.065), the rest of the time intervals (-2, 0, 1, 2) did not show significant effects. This suggests that the treatment’s timing relative to its initiation may not substantially affect child wasting.

Breastfeeding Frequency

The coefficient for breastfed_day_clean11-20 times was statistically significant with a positive estimate (0.071, p < 0.001), suggesting that children who are breastfed 11-20 times per day are more likely to experience a reduction in wasting compared to those with different breastfeeding patterns. This aligns with the existing literature on the protective effects of breastfeeding for child health outcomes. However, other categories of breastfeeding frequency (1-10 times and 21 or more times) did not show significant effects, which may imply that there is an optimal breastfeeding frequency for addressing child wasting.

7. Conclusion

Summary of Results

The results suggest that the growth monitoring program (GMP) in this study did not have a significant overall impact on child wasting. The non-significance of the treated variable and the lack of significant effects from the timing of treatment suggest that other factors may be more important drivers of child wasting in this context.

However, breastfeeding frequency emerged as an important factor, with children breastfed 11-20 times per day exhibiting a statistically significant reduction in wasting. This finding highlights the potential importance of breastfeeding practices in mitigating child wasting, which warrants further attention in future public health interventions.

Internal and External Validity

The study’s internal validity is supported by the use of fixed effects to control for unobserved heterogeneity at the individual and time levels. However, the inclusion of external shocks (e.g., the earthquake) and the observed sensitivity of the treatment effects to the inclusion of 2015 raises concerns about the robustness of the results. While the control variables provided valuable insights into potential mediators of child wasting (e.g., breastfeeding), the lack of significant effects from the treatment variable suggests that other unobserved factors or program design elements may be driving the outcomes. Moreover, the non-significant findings could also be indicative of measurement issues, such as inaccuracies in capturing treatment dosage or timing.

The external validity of the study may be limited by the contextual factors surrounding the intervention, particularly the impact of the earthquake. The results from this study may not be easily generalized to other contexts where the external environment, such as natural disasters or political instability, does not introduce similar disruptions. Additionally, the potential for selection bias due to sample attrition in 2015 may affect the generalizability of the findings. The sample population may not fully represent the broader population, particularly in regions most affected by the earthquake, which could limit the extent to which these findings can be extrapolated to other areas.

Policy Implications

The findings of this study suggest important policy implications for the Nepalese government’s efforts to combat malnutrition, particularly through the Growth Monitoring Program (GMP) within the National Multi-Sector Nutrition Plan (MSNP). While the GMP has the potential to contribute to reducing child wasting, the results highlight the need for more targeted, context-specific interventions.

1. Focus on Breastfeeding Practices:
   Given the significant association between increased breastfeeding frequency and reduced wasting, policymakers should consider emphasizing breastfeeding education and support as part of the GMP. This could include expanding nutrition counseling programs for caregivers and integrating breastfeeding promotion into community health workers’ regular activities.

2. Refining the GMP Design:
   The non-significant results from the treatment variable suggest that the current design of the GMP may need to be refined. Policymakers should consider evaluating the program’s implementation process, ensuring that growth monitoring is timely and that interventions are appropriately targeted to children at risk. In particular, improving the accuracy and consistency of treatment delivery could enhance program effectiveness.

3. Addressing External Shocks:
   The impact of the 2015 earthquake on participation and treatment dynamics suggests that external shocks can undermine the effectiveness of nutrition programs. Future policy should account for such disruptions by incorporating flexibility in program design and exploring strategies to maintain program engagement during crises. This could include preparedness measures and contingency planning for natural disasters or other shocks.

4. Expanding Multi-Sectoral Approaches:
   The MSNP’s multi-sectoral approach is integral to addressing the root causes of malnutrition. The findings emphasize the importance of this approach, suggesting that combining growth monitoring with other complementary interventions, such as improving household income, enhancing access to healthcare, and promoting sanitation, could have a more substantial impact on child health outcomes.

Next Steps

While the study’s findings do not provide strong evidence for the effectiveness of the GMP on child wasting, the results underscore the need for more targeted interventions, particularly those that focus on optimizing breastfeeding practices. Future research should aim to refine program interventions to better address the multifactorial nature of child wasting, considering both nutritional practices and other social determinants of health.

In addition, future research should further investigate how external shocks, such as natural disasters, can confound the effects of health interventions. Incorporating these factors into the analysis may provide a more accurate understanding of how such shocks influence the effectiveness of public health programs. Additionally, larger sample sizes and alternative methodological approaches may be necessary to definitively assess the role of GMPs in addressing child wasting, particularly in light of the variability introduced by external events like earthquakes.

By addressing these gaps and considering both internal and external factors that affect treatment outcomes, future research can offer more robust insights into the efficacy of growth monitoring programs in improving child health outcomes and inform the design of more effective policies in Nepal and beyond.

8. References

Johns Hopkins University. (2024). County-level predictors of COVID-19 vaccination rates in the United States. Johns Hopkins Data Archive. Retrievd from https://doi.org/10.7281/T1/UMDMQK

Nepal Health Research Council. (n.d.). PoSHAN Community Studies. Retrieved from https://elibrary.nhrc.gov.np/handle/20.500.14356/193

9. Appendices

Descriptive statistics

Across all regions, children aged 1 and 2 form the largest participant groups, with the highest counts in the Terai (526 for age 1) and Hills (514 for age 1). Participation decreases with increasing age, with minimal representation in the 6-year age group across all regions.

Across all regions, boys generally have slightly higher participation rates than girls. However, a significant decrease in participation is observed in 2015, particularly in the Hills and Mountains, likely due to the devastating earthquake that year, which heavily impacted these regions.

Boys consistently have higher wasting rates compared to girls throughout the period, with both genders showing a steady decline over time.

Among “Not wasted” children, 47.16% were not breastfed, while 33.71% were breastfed 1-10 times. In the “Wasted” group, the percentage of children not breastfed decreases to 34.44%, with higher proportions in the 1-10 times (37.84%) and other breastfeeding categories, reflecting different feeding patterns.

Household Income by GMP Participation

Growth Program Category	Min Income	1st Quartile	Median Income	Average Income	3rd Quartile	Max Income	Count
0 (No Participation)	0	0	2000	17598.30	12000	6200000	16091
1-3 (Common)	0	0	2000	16692.21	15000	6200000	2837
4-6 (Moderate)	0	0	3130	19109.99	20000	1100500	850
7+ (Rare)	0	0	2200	14434.04	15000	272000	167

Kids who were not wasted have the highest household median income at NPR 2,000 and an average income of NPR 18,218. On the other hand, kids with severe wasting come from households with much lower incomes, with a median of just NPR 900 and an average of NPR 8,405.

The distribution shows that most mothers fall within the 20 to 30 age range for both categories, with “Not wasted” children associated with a higher overall count of mothers in this range. The distribution tapers off significantly for mothers over 40, indicating fewer mothers of older age in both groups.

In all years, most participants had little to no engagement, with the majority concentrated at the lowest levels of participation. This pattern remains consistent across the years, indicating limited changes in overall program involvement.

Most cases of wasting are in the 0-4 age group, with 2,628 children affected, while only 556 cases are seen in the 5-9 group. Younger kids clearly face higher risks,

Kids with no or low participation (0 or 1-3 sessions) tend to have higher wasting rates, especially in the Terai. For moderate participation (4-6 sessions), the Hills saw a spike in 2015 with 41.7%. The data suggests that more frequent participation in the program could help lower wasting rates, especially in high-risk regions.

The average income is highest for households classified under “Not wasted” (18,218.35 NPR) and lowest for “Severe wasting” (8,405.05 NPR). Median incomes also decrease progressively across categories, with “Not wasted” having a median income of 2,000 NPR, while “Severe wasting” has the lowest at 900 NPR.

While the median income looks similar for all three, Terai shows a much wider range in income.

Across Nepal’s three geographical regions: the northern Mountain, middle Hills, and southern Terai. Districts in the Mountain region, like Mugu, show relatively higher average incomes, marked in yellow. In contrast, many districts in the Hills and Terai, such as Rolpa and Bajhang, display lower average incomes, represented by deep blue.

Certain regions, such as Mugu and Solukhumbu, show higher levels of involvement, while others, like Morang and Sarlahi, have lower participation rates.